Exploiting CF for object tracking started with the method called the minimum output sum of squared error, i.e., MOSSE tracker [1]}. The tracker is constructed and trained using gray-scale samples in the frequency domain for efficiency. Afterward, a variety of works built upon the framework improve the performance by combining multiple features [2]}, scale estimation [3]}, kernel trick [4]}, context learning [5]}, [6]}, filter weighing [7]}, [8]}, [9]}, [10]}, or end-to-end neural network architecture [11]}, [12]}, [13]}. Kernel cross-correlator (KCC) [14]} provides a novel solution for the CF-based framework with high expandability and brief formulation. However, the lack of context information and further refinement of generated responses limit the tracker's ability to discriminate the object in diversified environments.
